Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.
# S3 method for cv.glmnet tidy(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other glmnet tidiers: glance.cv.glmnet
,
glance.glmnet
, tidy.glmnet
A tibble::tibble()
with columns:
Value of penalty parameter lambda.
Number of coefficients that are exactly zero for given lambda.
The standard error of the regression term.
lower bound on confidence interval for cross-validation estimated loss.
upper bound on confidence interval for cross-validation estimated loss.
Median loss across all cross-validation folds for a given lamdba
library(glmnet) set.seed(27) nobs <- 100 nvar <- 50 real <- 5 x <- matrix(rnorm(nobs * nvar), nobs, nvar) beta <- c(rnorm(real, 0, 1), rep(0, nvar - real)) y <- c(t(beta) %*% t(x)) + rnorm(nvar, sd = 3) cvfit1 <- cv.glmnet(x,y) tidy(cvfit1)#> # A tibble: 73 x 6 #> lambda estimate std.error conf.low conf.high nzero #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 1.45 17.3 1.96 15.3 19.3 0 #> 2 1.32 17.2 1.98 15.2 19.1 1 #> 3 1.20 16.9 1.95 15.0 18.9 1 #> 4 1.09 16.7 1.93 14.8 18.6 1 #> 5 0.997 16.7 1.92 14.8 18.6 1 #> 6 0.909 16.8 1.91 14.9 18.7 2 #> 7 0.828 16.9 1.92 15.0 18.8 3 #> 8 0.754 17.0 1.94 15.1 18.9 5 #> 9 0.687 17.1 1.96 15.2 19.1 7 #> 10 0.626 17.3 1.98 15.3 19.3 7 #> # ... with 63 more rowsglance(cvfit1)#> # A tibble: 1 x 2 #> lambda.min lambda.1se #> <dbl> <dbl> #> 1 0.997 1.45library(ggplot2) tidied_cv <- tidy(cvfit1) glance_cv <- glance(cvfit1) # plot of MSE as a function of lambda g <- ggplot(tidied_cv, aes(lambda, estimate)) + geom_line() + scale_x_log10() g# plot of MSE as a function of lambda with confidence ribbon g <- g + geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25) g# plot of MSE as a function of lambda with confidence ribbon and choices # of minimum lambda marked g <- g + geom_vline(xintercept = glance_cv$lambda.min) + geom_vline(xintercept = glance_cv$lambda.1se, lty = 2) g# plot of number of zeros for each choice of lambda ggplot(tidied_cv, aes(lambda, nzero)) + geom_line() + scale_x_log10()# coefficient plot with min lambda shown tidied <- tidy(cvfit1$glmnet.fit) ggplot(tidied, aes(lambda, estimate, group = term)) + scale_x_log10() + geom_line() + geom_vline(xintercept = glance_cv$lambda.min) + geom_vline(xintercept = glance_cv$lambda.1se, lty = 2)